What We Implement
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CX AI that works in production.
Not just in the vendor demo.
Most organizations deploy AI as a layer on top of an existing environment. We implement it as part of the environment — scoped, configured, and tested before the platform goes live.
Platform Coverage
The full CCaaS and CRM AI ecosystem. One implementation partner.
CCaaS AI
NICE CXone
Enlighten AI, CXone Mpower, auto QM, agent assist, forecasting.
Cognigy
Conversational AI, agentic AI, virtual agents, agent copilot, knowledge AI.
Genesys AI
Virtual agent, predictive routing, agent copilot, speech analytics.
Five9 Genius AI
Virtual agent, agent assist, voice biometrics, workflow automation.
Amazon Connect
Lex bots, Contact Lens, AI-driven routing, real-time analytics.
CRM & Productivity AI
Salesforce Einstein / Agentforce
Einstein AI, Agentforce, next-best-action, AI-powered case management, CX integration.
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Microsoft Copilot
Custom agents, M365 integration, contact center automation.
Zoom AI Companion
AI Companion, virtual agent, real-time coaching, post-call summaries.
How We Deploy
Why most AI deployments stall — and how we prevent it.
Most AI deployments underperform because the implementation treated AI as a configuration task, not an architectural one. Intents built without reference to actual call drivers. Integration deferred. Performance measured against vendor benchmarks that do not reflect the operation.
The result is a system at 30–40% of its potential with no clear path forward. Getting past that requires a foundation built correctly from the start.
Implementation quality determines the ceiling. Optimization determines how fast you reach it.
Scoped before configured
The AI use case is defined and data requirements understood before any configuration begins. Deploying without this produces a system that works in testing and underperforms in production.
Integrated from the start
AI is connected to the CRM, the CCaaS platform, and the data sources it needs at build time. Integration is not a follow-up task.
Tested under production conditions
AI behavior is validated against real call types, real intents, and real escalation paths before go-live. Performance is baselined before handover.
Handed over with a documented foundation
Intent library, flow logic, escalation paths, and performance baselines documented at handover. The team that receives the environment understands what was built and why.
Client Evidence
AI in production. Built to perform.
AI–CCaaS Integration
IBM
IBM needed Watson AI to manage 75,000+ healthcare inquiries per month with a 90% deflection target. One Primero engineered an integration that had never been built before.
Read the Full Case Study →
Team Augmentation
Global Sports Performance Platform
50+ engineers embedded into a global sports technology company to build an AI-driven performance platform serving trainers, coaches, and players across 122 countries.
Read the Full Case Study →Related Capabilities
The full CX platform capability. End to end.
AI Managed Services
Deployment gets the AI live. Managed services keeps it performing. Continuous optimization, intent training, and structured performance review after go-live.
CCaaS Implementation
The platform the AI runs on. NiCE CXone, Five9, Genesys, Zoom CC. AI scoped and configured from the first sprint, not added after go-live.
CX Strategy & Advisory
AI readiness scored as a named dimension before any deployment begins. The assessment that tells you what has to be in place before the first agent goes live.
The contact center AI layer, built for how your operation runs.
Tell us which platform you are on and what the AI is supposed to do. We will tell you how to build it right the first time.
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